
Proceedings Paper
Integrating shape into an interactive segmentation frameworkFormat | Member Price | Non-Member Price |
---|---|---|
$17.00 | $21.00 |
Paper Abstract
This paper presents a novel interactive annotation toolbox which extends a well-known user-steered segmentation
framework, namely Intelligent Scissors (IS). IS, posed as a shortest path problem, is essentially driven by lower level
image based features. All the higher level knowledge about the problem domain is obtained from the user through mouse clicks. The proposed work integrates one higher level feature, namely shape up to a rigid transform, into the IS
framework, thus reducing the burden on the user and the subjectivity involved in the annotation procedure, especially
during instances of occlusions, broken edges, noise and spurious boundaries. The above mentioned scenarios are
commonplace in medical image annotation applications and, hence, such a tool will be of immense help to the medical
community. As a first step, an offline training procedure is performed in which a mean shape and the corresponding
shape variance is computed by registering training shapes up to a rigid transform in a level-set framework. The user
starts the interactive segmentation procedure by providing a training segment, which is a part of the target boundary. A partial shape matching scheme based on a scale-invariant curvature signature is employed in order to extract shape
correspondences and subsequently predict the shape of the unsegmented target boundary. A ‘zone of confidence’ is
generated for the predicted boundary to accommodate shape variations. The method is evaluated on segmentation of
digital chest x-ray images for lung annotation which is a crucial step in developing algorithms for screening Tuberculosis.
Paper Details
Date Published: 28 February 2013
PDF: 14 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867030 (28 February 2013); doi: 10.1117/12.2007262
Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)
PDF: 14 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867030 (28 February 2013); doi: 10.1117/12.2007262
Show Author Affiliations
S. Kamalakannan, Texas Tech Univ. (United States)
B. Bryant, Texas Tech Univ. (United States)
H. Sari-Sarraf, Texas Tech Univ. (United States)
B. Bryant, Texas Tech Univ. (United States)
H. Sari-Sarraf, Texas Tech Univ. (United States)
Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)
© SPIE. Terms of Use
